Pmax_____________________________________
Access results
summary(canopy_pmax_results$models[[1]]) # Model 1 summary (salinity)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 919.7 932.5 -454.8 909.7 91
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.79668 -0.55604 -0.09338 0.55720 2.60374
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 199.9 14.14
## rlc_order (Intercept) 157.1 12.53
## Residual 462.6 21.51
## Number of obs: 96, groups: plantID, 48; rlc_order, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 101.765 4.821 50.311 21.107 <2e-16 ***
## salinity35 -7.549 6.116 39.693 -1.234 0.224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.634
summary(canopy_pmax_results$models[[2]]) # Model 2 summary (nitrate)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 920.2 938.2 -453.1 906.2 89
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.71404 -0.57772 -0.06531 0.66096 2.58147
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 169.9 13.03
## rlc_order (Intercept) 147.3 12.14
## Residual 466.1 21.59
## Number of obs: 96, groups: plantID, 48; rlc_order, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 109.257 6.530 50.666 16.732 <2e-16 ***
## nitrate2 -15.133 8.761 47.771 -1.727 0.0906 .
## nitrate3 -19.788 9.144 50.888 -2.164 0.0352 *
## nitrate4 -10.144 8.761 47.771 -1.158 0.2527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.671
## nitrate3 -0.700 0.522
## nitrate4 -0.671 0.455 0.522
summary(canopy_pmax_results$model_all) # Model 3 summary (salinity and nitrate)
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 920.6 941.1 -452.3 904.6 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.86204 -0.55814 -0.01586 0.65726 2.69009
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 151.4 12.31
## rlc_order (Intercept) 154.7 12.44
## Residual 463.5 21.53
## Number of obs: 96, groups: plantID, 48; rlc_order, 32
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 113.054 7.048 51.337 16.040 <2e-16 ***
## salinity35 -7.581 5.773 39.262 -1.313 0.1967
## nitrate2 -15.145 8.586 47.480 -1.764 0.0842 .
## nitrate3 -19.850 8.989 51.181 -2.208 0.0317 *
## nitrate4 -10.095 8.586 47.480 -1.176 0.2456
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.410
## nitrate2 -0.609 0.000
## nitrate3 -0.638 0.000 0.523
## nitrate4 -0.609 0.000 0.452 0.523
canopy_pmax_results$anova_result[[1]] # ANOVA did nitrate have an effect on Pmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 5 919.70 932.52 -454.85 909.70
## model_all 8 920.57 941.09 -452.29 904.57 5.1236 3 0.163
canopy_pmax_results$anova_result[[2]] # ANOVA did salinity have an effect on Pmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 7 920.25 938.20 -453.12 906.25
## model_all 8 920.57 941.09 -452.29 904.57 1.6766 1 0.1954
check_model_fit(canopy_pmax_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.08265697 0.44759




## $salinity

##
## $nitrate

Inputs for Understory/Pmax
under_pmax_results <- compare_lmer_models(
data = under,
response = "pmax",
predictors_list,
random_effects = c("mean_mins28", "rlc_order")
)
Access results
summary(under_pmax_results$models[[1]]) # Model 1 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 883.6 896.5 -436.8 873.6 91
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.39721 -0.58387 -0.02647 0.59074 2.70764
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 15.89 3.986
## mean_mins28 (Intercept) 13.23 3.637
## Residual 501.46 22.393
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 75.62709 4.26002 4.33413 17.753 3.26e-05 ***
## salinity35 0.05666 4.61214 90.54020 0.012 0.99
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.541
summary(under_pmax_results$models[[2]]) # Model 2 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 886.8 904.7 -436.4 872.8 89
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37932 -0.63396 -0.05697 0.61647 2.84997
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 12.06 3.473
## mean_mins28 (Intercept) 13.05 3.613
## Residual 500.01 22.361
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 79.057 5.383 9.864 14.687 4.99e-08 ***
## nitrate2 -6.098 6.598 79.586 -0.924 0.358
## nitrate3 -4.421 6.694 45.972 -0.660 0.512
## nitrate4 -3.088 6.598 79.586 -0.468 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.613
## nitrate3 -0.622 0.507
## nitrate4 -0.613 0.485 0.507
summary(under_pmax_results$model_all) # Model 3 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 888.8 909.3 -436.4 872.8 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37809 -0.63522 -0.05697 0.61709 2.85120
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 12.06 3.472
## mean_mins28 (Intercept) 13.05 3.613
## Residual 500.01 22.361
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 79.02837 5.85285 13.55302 13.503 3.03e-09 ***
## salinity35 0.05736 4.59678 90.32593 0.012 0.990
## nitrate2 -6.09755 6.59829 79.58591 -0.924 0.358
## nitrate3 -4.42113 6.69434 45.97127 -0.660 0.512
## nitrate4 -3.08787 6.59829 79.58591 -0.468 0.641
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.393
## nitrate2 -0.564 0.000
## nitrate3 -0.572 0.000 0.507
## nitrate4 -0.564 0.000 0.485 0.507
under_pmax_results$anova_result[[1]] # ANOVA did nitrate have an effect on Pmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 5 883.65 896.47 -436.82 873.65
## model_all 8 888.75 909.27 -436.38 872.75 0.8926 3 0.8272
under_pmax_results$anova_result[[2]] # ANOVA did salinity have an effect on Pmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 7 886.75 904.70 -436.38 872.75
## model_all 8 888.75 909.27 -436.38 872.75 2e-04 1 0.99
check_model_fit(under_pmax_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.009520906 0.05687984




## $salinity

##
## $nitrate

NPQmax______________________________
Access results
summary(canopy_npqmax_results$models[[1]]) # Model 1 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 65.0 80.4 -26.5 53.0 90
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5570 -0.4536 -0.1271 0.4531 4.0118
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 0.042708 0.20666
## rlc_order (Intercept) 0.001894 0.04352
## mean_mins28 (Intercept) 0.012323 0.11101
## Residual 0.062045 0.24909
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.71940 0.09643 3.59648 7.460 0.0026 **
## salinity35 -0.03194 0.07855 46.01523 -0.407 0.6862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.407
summary(canopy_npqmax_results$models[[2]]) # Model 2 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 42.4 62.9 -13.2 26.4 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1222 -0.4656 0.0082 0.4043 5.2251
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 0.0105110 0.10252
## rlc_order (Intercept) 0.0008604 0.02933
## mean_mins28 (Intercept) 0.0117902 0.10858
## Residual 0.0630749 0.25115
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.00567 0.09725 4.06386 10.341 0.000455 ***
## nitrate2 -0.36564 0.08413 46.87709 -4.346 7.40e-05 ***
## nitrate3 -0.37748 0.08442 41.65616 -4.472 5.87e-05 ***
## nitrate4 -0.46583 0.08413 46.87709 -5.537 1.35e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.433
## nitrate3 -0.434 0.502
## nitrate4 -0.433 0.497 0.502
summary(canopy_npqmax_results$model_all) # Model 3 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 44.1 67.1 -13.0 26.1 87
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1856 -0.4654 0.0101 0.3926 5.1965
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 0.0102212 0.1011
## rlc_order (Intercept) 0.0006758 0.0260
## mean_mins28 (Intercept) 0.0117925 0.1086
## Residual 0.0632712 0.2515
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.02164 0.10151 4.79409 10.064 0.00021 ***
## salinity35 -0.03210 0.05914 45.32554 -0.543 0.58987
## nitrate2 -0.36563 0.08386 46.79540 -4.360 7.08e-05 ***
## nitrate3 -0.37723 0.08408 41.28225 -4.487 5.68e-05 ***
## nitrate4 -0.46578 0.08386 46.79540 -5.555 1.28e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.291
## nitrate2 -0.413 0.000
## nitrate3 -0.414 0.000 0.501
## nitrate4 -0.413 0.000 0.497 0.501
canopy_npqmax_results$anova[[1]] # ANOVA did nitrate have an effect on NPQmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 6 64.984 80.371 -26.492 52.984
## model_all 9 44.068 67.147 -13.034 26.068 26.916 3 6.131e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
canopy_npqmax_results$anova[[2]] # ANOVA did salinity have an effect on NPQmax?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 8 42.362 62.876 -13.181 26.362
## model_all 9 44.068 67.147 -13.034 26.068 0.2933 1 0.5881
check_model_fit(canopy_npqmax_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.2745407 0.4660269




## $salinity

##
## $nitrate

Inputs for Understory/NPQmax
under_npqmax_results <- compare_lmer_models(
data = under,
response = "maxNPQ_Ypoint1",
predictors_list,
random_effects = c("plantID")
)
deltaNPQ____________________________
Access results
summary(canopy_delta_npq_results$models[[1]]) # Model 1 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 46.9 62.2 -17.4 34.9 90
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7942 -0.5197 -0.1043 0.3128 4.0319
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 3.962e-02 1.990e-01
## rlc_order (Intercept) 8.754e-12 2.959e-06
## mean_mins28 (Intercept) 5.137e-03 7.167e-02
## Residual 5.122e-02 2.263e-01
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.53806 0.07271 5.43747 7.401 0.00049 ***
## salinity35 -0.02440 0.07373 47.53406 -0.331 0.74218
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.507
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
summary(canopy_delta_npq_results$models[[2]]) # Model 2 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 23.8 44.3 -3.9 7.8 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5427 -0.5460 -0.1675 0.3312 5.1358
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 1.125e-02 0.106065
## rlc_order (Intercept) 5.221e-05 0.007225
## mean_mins28 (Intercept) 4.698e-03 0.068543
## Residual 5.122e-02 0.226322
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.81300 0.07365 6.64427 11.039 1.62e-05 ***
## nitrate2 -0.36960 0.07841 47.15770 -4.714 2.19e-05 ***
## nitrate3 -0.35376 0.07843 45.45966 -4.511 4.53e-05 ***
## nitrate4 -0.42517 0.07841 47.15770 -5.423 1.97e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.532
## nitrate3 -0.532 0.500
## nitrate4 -0.532 0.500 0.500
summary(canopy_delta_npq_results$model_all) # Model 3 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 25.6 48.7 -3.8 7.6 87
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.5850 -0.5396 -0.1675 0.3021 5.1086
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 0.011108 0.10540
## rlc_order (Intercept) 0.000000 0.00000
## mean_mins28 (Intercept) 0.004698 0.06854
## Residual 0.051264 0.22642
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.82516 0.07859 8.39384 10.500 4.10e-06 ***
## salinity35 -0.02440 0.05533 47.40510 -0.441 0.661
## nitrate2 -0.36958 0.07825 47.40510 -4.723 2.10e-05 ***
## nitrate3 -0.35367 0.07825 47.40510 -4.520 4.13e-05 ***
## nitrate4 -0.42513 0.07825 47.40510 -5.433 1.88e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.352
## nitrate2 -0.498 0.000
## nitrate3 -0.498 0.000 0.500
## nitrate4 -0.498 0.000 0.500 0.500
## optimizer (nloptwrap) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')
canopy_delta_npq_results$anova[[1]] # ANOVA did nitrate have an effect on deltaNPQ?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 6 46.863 62.249 -17.4315 34.863
## model_all 9 25.614 48.693 -3.8069 7.614 27.249 3 5.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
canopy_delta_npq_results$anova[[2]] # ANOVA did salinity have an effect on deltaNPQ?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 8 23.808 44.322 -3.9038 7.8077
## model_all 9 25.614 48.693 -3.8069 7.6137 0.1939 1 0.6597
check_model_fit(canopy_delta_npq_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.2991261 0.4643007




## $salinity

##
## $nitrate

Inputs for Understory/deltaNPQ
under_delta_npq_results <- compare_lmer_models(
data = under,
response = "deltaNPQ",
predictors_list,
random_effects = c("plantID")
)
Ek____________________________________________
Access results
summary(canopy_ek_results$models[[1]]) # Model 1 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 993.6 1009.0 -490.8 981.6 90
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8311 -0.5932 -0.0605 0.5266 3.3316
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 361.2 19.01
## rlc_order (Intercept) 240.9 15.52
## mean_mins28 (Intercept) 150.7 12.28
## Residual 1040.1 32.25
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 177.380 10.964 3.239 16.179 0.000329 ***
## salinity35 -13.284 8.712 39.775 -1.525 0.135214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.397
summary(canopy_ek_results$models[[2]]) # Model 2 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 995.7 1016.2 -489.8 979.7 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1327 -0.5942 -0.0610 0.4520 3.0012
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 316.0 17.78
## rlc_order (Intercept) 258.3 16.07
## mean_mins28 (Intercept) 148.9 12.20
## Residual 1033.6 32.15
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 159.526 12.649 5.654 12.612 2.36e-05 ***
## nitrate2 12.348 12.498 46.880 0.988 0.3282
## nitrate3 7.310 12.981 50.099 0.563 0.5759
## nitrate4 25.190 12.498 46.880 2.016 0.0496 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.494
## nitrate3 -0.513 0.519
## nitrate4 -0.494 0.461 0.519
summary(canopy_ek_results$model_all) # Model 3 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 995.0 1018.1 -488.5 977.0 87
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94436 -0.66754 0.00851 0.56066 3.12130
##
## Random effects:
## Groups Name Variance Std.Dev.
## plantID (Intercept) 247.8 15.74
## rlc_order (Intercept) 301.7 17.37
## mean_mins28 (Intercept) 145.2 12.05
## Residual 1020.1 31.94
## Number of obs: 96, groups: plantID, 48; rlc_order, 32; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 166.014 13.063 6.446 12.709 8.39e-06 ***
## salinity35 -13.615 8.118 36.394 -1.677 0.1021
## nitrate2 12.618 12.067 45.994 1.046 0.3012
## nitrate3 7.773 12.626 50.791 0.616 0.5409
## nitrate4 25.734 12.067 45.994 2.133 0.0383 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.311
## nitrate2 -0.462 0.000
## nitrate3 -0.483 0.000 0.523
## nitrate4 -0.462 0.000 0.453 0.523
canopy_ek_results$anova[[1]] # ANOVA did nitrate have an effect on Ek?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 6 993.60 1009.0 -490.80 981.60
## model_all 9 995.04 1018.1 -488.52 977.04 4.5637 3 0.2067
canopy_ek_results$anova[[2]] # ANOVA did salinity have an effect on Ek?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 8 995.68 1016.2 -489.84 979.68
## model_all 9 995.04 1018.1 -488.52 977.04 2.6442 1 0.1039
check_model_fit(canopy_ek_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.07310292 0.448615




## $salinity

##
## $nitrate

Inputs for Understory/Ek
under_ek_results <- compare_lmer_models(
data = under,
response = "ek.est",
predictors_list,
random_effects = c("mean_mins28", "rlc_order")
)
Access results
summary(under_ek_results$models[[1]]) # Model 1 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 970.8 983.7 -480.4 960.8 91
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9867 -0.6906 -0.1270 0.6353 3.0333
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 36.832 6.069
## mean_mins28 (Intercept) 2.534 1.592
## Residual 1264.120 35.554
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 133.936 5.489 6.557 24.402 1.1e-07 ***
## salinity35 -6.767 7.318 89.907 -0.925 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## salinity35 -0.667
summary(under_ek_results$models[[2]]) # Model 2 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 969.7 987.7 -477.9 955.7 89
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.12670 -0.68008 -0.08205 0.63288 2.99702
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 99.844 9.992
## mean_mins28 (Intercept) 7.511 2.741
## Residual 1144.211 33.826
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 114.119 7.986 17.418 14.291 4.68e-11 ***
## nitrate2 19.059 10.416 85.624 1.830 0.0708 .
## nitrate3 25.003 10.856 55.420 2.303 0.0250 *
## nitrate4 21.671 10.416 85.624 2.081 0.0405 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) nitrt2 nitrt3
## nitrate2 -0.652
## nitrate3 -0.680 0.521
## nitrate4 -0.652 0.457 0.521
summary(under_ek_results$model_all) # Model 3 summary
## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: formula
## Data: data
##
## AIC BIC logLik deviance df.resid
## 970.8 991.3 -477.4 954.8 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.22521 -0.76232 -0.08919 0.57191 2.91970
##
## Random effects:
## Groups Name Variance Std.Dev.
## rlc_order (Intercept) 100.788 10.039
## mean_mins28 (Intercept) 7.803 2.793
## Residual 1131.695 33.641
## Number of obs: 96, groups: rlc_order, 16; mean_mins28, 2
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 117.457 8.703 23.877 13.496 1.15e-12 ***
## salinity35 -6.748 7.004 86.873 -0.964 0.3379
## nitrate2 19.080 10.369 85.738 1.840 0.0692 .
## nitrate3 25.086 10.813 55.664 2.320 0.0240 *
## nitrate4 21.713 10.369 85.738 2.094 0.0392 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) slnt35 nitrt2 nitrt3
## salinity35 -0.402
## nitrate2 -0.596 0.000
## nitrate3 -0.621 0.000 0.521
## nitrate4 -0.596 0.000 0.456 0.521
under_ek_results$anova[[1]] # ANOVA did nitrate have an effect on Ek?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 5 970.83 983.65 -480.41 960.83
## model_all 8 970.79 991.30 -477.39 954.79 6.0408 3 0.1096
under_ek_results$anova[[2]] # ANOVA did salinity have an effect on Ek?
## Data: data
## Models:
## models[[i]]: formula
## model_all: formula
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## models[[i]] 7 969.71 987.66 -477.86 955.71
## model_all 8 970.79 991.30 -477.39 954.79 0.9237 1 0.3365
check_model_fit(under_ek_results$model_all, terms = predictors_list)
## R2m R2c
## [1,] 0.07972884 0.1603012




## $salinity

##
## $nitrate
